


'''simple docstring'''
from statistics import mean

import numpy as np




def 	_A  (       A							,A							,A							,A							)  ->				Optional[int]:
	lowercase						:		Dict   					=		0
	# Number of processes finished
	lowercase						:		Tuple   					=		0
	# Displays the finished process.
	# If it is 0, the performance is completed if it is 1, before the performance.
	lowercase						:		Any   					=		[0] * no_of_process
	# List to include calculation results
	lowercase						:		Union[str, Any]   					=		[0] * no_of_process

	# Sort by arrival time.
	lowercase						:		Union[str, Any]   					=		[burst_time[i] for i in np.argsort(a__							)]
	lowercase						:		Union[str, Any]   					=		[process_name[i] for i in np.argsort(a__							)]
	arrival_time.sort()

	while no_of_process > finished_process_count:
		lowercase						:		Optional[Any]   					=		0
		while finished_process[i] == 1:
			i += 1
		if current_time < arrival_time[i]:
			lowercase						:		Optional[Any]   					=		arrival_time[i]

		lowercase						:		Optional[Any]   					=		0
		# Index showing the location of the process being performed
		lowercase						:		Optional[int]   					=		0
		# Saves the current response ratio.
		lowercase						:		Tuple   					=		0
		for i in range(0							,a__							):
			if finished_process[i] == 0 and arrival_time[i] <= current_time:
				lowercase						:		List[Any]   					=		(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
				    i
				]
			if response_ratio < temp:
				lowercase						:		int   					=		temp
				lowercase						:		List[str]   					=		i

        # Calculate the turn around time
		lowercase						:		int   					=		current_time + burst_time[loc] - arrival_time[loc]
		current_time += burst_time[loc]
		# Indicates that the process has been performed.
		lowercase						:		Dict   					=		1
		# Increase finished_process_count by 1
		finished_process_count += 1

	return turn_around_time




def 	_A  (       A							,A							,A							,A							)  ->				Tuple:
	lowercase						:		Tuple   					=		[0] * no_of_process
	for i in range(0							,a__							):
		lowercase						:		Optional[int]   					=		turn_around_time[i] - burst_time[i]
	return waiting_time


if __name__ == "__main__":
					lowerCAmelCase   : Union[str, Any]             =				5
					lowerCAmelCase   : Union[str, Any]             =				["""A""", """B""", """C""", """D""", """E"""]
					lowerCAmelCase   : int             =				[1, 2, 3, 4, 5]
					lowerCAmelCase   : Optional[int]             =				[1, 2, 3, 4, 5]

					lowerCAmelCase   : Tuple             =				calculate_turn_around_time(
					    process_name, arrival_time, burst_time, no_of_process
					)
					lowerCAmelCase   : str             =				calculate_waiting_time(
					    process_name, turn_around_time, burst_time, no_of_process
					)

					print("""Process name \tArrival time \tBurst time \tTurn around time \tWaiting time""")
					for i in range(0, no_of_process):
										print(
										    F'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
										    F'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
										)

					print(F'''average waiting time : {mean(waiting_time):.5f}''')
					print(F'''average turn around time : {mean(turn_around_time):.5f}''')





from random import randint
from tempfile import TemporaryFile

import numpy as np


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						List[Any] 							= 0
			if start < end:
						a_						:						Dict 							= randint(a__  ,	a__)
						a_						:						List[str] 							= a[end]
						a_						:						Tuple 							= a[pivot]
						a_						:						Tuple 							= temp

						a_							,					a_						:						List[Any] 							= _in_place_partition(a__  ,	a__  ,	a__)
						count += _in_place_quick_sort(a__  ,	a__  ,	p - 1)
						count += _in_place_quick_sort(a__  ,	p + 1  ,	a__)
			return count


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						Optional[int] 							= 0
			a_						:						Union[str, Any] 							= randint(a__  ,	a__)
			a_						:						Union[str, Any] 							= a[end]
			a_						:						Any 							= a[pivot]
			a_						:						Any 							= temp
			a_						:						int 							= start - 1
			for index in range(a__  ,	a__):
						count += 1
						if a[index] < a[end]:  # check if current val is less than pivot value
									a_						:						str 							= new_pivot_index + 1
									a_						:						Optional[Any] 							= a[new_pivot_index]
									a_						:						str 							= a[index]
									a_						:						Union[str, Any] 							= temp

			a_						:						Union[str, Any] 							= a[new_pivot_index + 1]
			a_						:						Tuple 							= a[end]
			a_						:						Any 							= temp
			return new_pivot_index + 1, count


__snake_case				:      Union[str, Any]   		=			TemporaryFile()
__snake_case				:      Dict   		=			1_00  # 1000 elements are to be sorted


__snake_case		, __snake_case				:      int   		=			0, 1  # mean and standard deviation
__snake_case				:      Tuple   		=			np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)


outfile.seek(0)  # using the same array
__snake_case				:      str   		=			np.load(outfile)
__snake_case				:      Dict   		=			len(M) - 1
__snake_case				:      Dict   		=			_in_place_quick_sort(M, 0, r)

print(
    """No of Comparisons for 100 elements selected from a standard normal distribution"""
    """is :"""
)



print(z)


